Showing papers by "Andrea Sciarrone published in 2013"

Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications

Abstract: This paper proposes a system that allows recognizing a person's emotional state starting from audio signal registrations. The provided solution is aimed at improving the interaction among humans and computers, thus allowing effective human-computer intelligent interaction. The system is able to recognize six emotions(anger, boredom, disgust, fear, happiness, and sadness) and the neutral state. This set of emotional states is widely used for emotion recognition purposes. It also distinguishes a single emotion versus all the other possible ones, as proven in the proposed numerical results. The system is composed of two subsystems: 1) gender recognition(GR) and 2) emotion recognition(ER). The experimental analysis shows the performance in terms of accuracy of the proposed ER system. The results highlight that the a priori knowledge of the speaker's gender allows a performance increase. The obtained results show also that the features selection adoption assures a satisfying recognition rate and allows reducing the employed features. Future developments of the proposed solution may include the implementation of this system over mobile devices such as smartphones.

GPS/HPS-and Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphones in Cloud-Based LBSs

Abstract: This paper proposes a new location recognition algorithm for automatic check-in applications (LRACI), suited to be implemented within Smartphones, integrated in the Cloud platform and representing a service for Cloud end users. The algorithm, the performance of which is independent of the employed device, uses both global and hybrid positioning systems (GPS/HPS) and, in an opportunistic way, the presence of Wi-Fi access points (APs), through a new definition of Wi-Fi FingerPrint (FP), which is proposed in this paper. This FP definition considers the order relation among the received signal strength (RSS) rather than the absolute values. This is one of the main contributions of this paper. LRACI is designed to be employed where traditional approaches, usually based only on GPS/HPS, fail, and is aimed at finding user location, with a room-level resolution, in order to estimate the overall time spent in the location, called Permanence, instead of the simple presence. LRACI allows automatic check-in in a given location only if the users' Permanence is larger than a minimum amount of time, called Stay Length (SL), and may be exploited in the Cloud. For example, if many people check-in in a particular location (e.g., a supermarket or a post office), it means that the location is crowded. Using LRACI-based data, collected by smartphones in the Cloud and made available in the Cloud itself, end users can manage their daily activities (e.g., buying food or paying a bill) in a more efficient way. The proposal, practically implemented over Android operating system-based Smartphones, has been extensively tested. Experimental results have shown a location recognition accuracy of about 90%, opening the door to real LRACI employments. In this sense, a preliminary study of its application in the Cloud, obtained through simulation, has been provided to highlight the advantages of the LRACI features.

Energy efficient WiFi-based fingerprinting for indoor positioning with smartphones

Abstract: The paper presents an energy efficient WiFi-based indoor positioning algorithm, based on the probabilistic fingerprinting method, suited to be used over smartphone platforms. The work proposes a simple algebraic approach aimed at reducing the computational and energy loads of the probabilistic fingerprinting, which is employed to carry out the position of a smartphone on the basis of the captured WiFi Access Points signal strengths in an indoor area. The presented solution does not apply any kind of approximation with respect to the traditional approach, so avoiding accuracy detriment. The idea is to factoring out the parts of the probabilistic fingerprint formulae that can be computed a-priori, so reducing the computational burden of the positioning process. The paper also highlights the effectiveness of the proposed solution by evaluating the energy saved in comparison with the traditional approach. The obtained results, collected by testing three different smartphones, show that our approach allows saving a significant quantity of energy so increasing the smartphones battery lifetime.

Performance comparison of a probabilistic fingerprint-based indoor positioning system over different smartphones

Abstract: In this paper a performance comparison of a probabilistic Gaussian-Kernel fingerprint-based indoor positioning method over different smartphones, is presented. The work aims at highlighting the positioning accuracy, the robustness and the consistency of the method by testing it over two different smartphone platforms (i.e., Nokia N95 and Samsung Galaxy S II), within a given area. In more detail, three different variants of the probabilistic approach have been tested: Nearest Neighbor (NN), K-Nearest Neighbor (K-NN) and K Weighted-Nearest Neighbor (KW-NN). Numerical experiments, carried out in an area of around 80 [m2], have shown that the probabilistic fingerprint provides good position accuracy (less than 1.20 [m] of error) for both devices and also robustness when the signal strength acquisitions are reduced. Finally, the similarity of results provided by the two smartphones leads to assert that the probabilistic approach is also consistent with respect to the device employed in the experiments.